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from PIL import Image
import numpy as np
from rembg import remove
import cv2
import os
from torchvision.transforms import GaussianBlur
import gradio as gr
import replicate
import requests
from io import BytesIO
def create_mask(input):
input_path = 'input.png'
bg_removed_path = 'bg_removed.png'
mask_name = 'blured_mask.png'
input.save(input_path)
bg_removed = remove(input)
bg_removed = bg_removed.resize((512, 512))
bg_removed.save(bg_removed_path)
img2_grayscale = bg_removed.convert('L')
img2_a = np.array(img2_grayscale)
mask = np.array(img2_grayscale)
threshhold = 0
mask[img2_a==threshhold] = 1
mask[img2_a>threshhold] = 0
strength = 1
d = int(255 * (1-strength))
mask *= 255-d
mask += d
mask = Image.fromarray(mask)
blur = GaussianBlur(11,20)
mask = blur(mask)
mask = mask.resize((512, 512))
mask.save(mask_name)
return Image.open(mask_name)
def generate_image(image, product_name, target_name):
mask = create_mask(image)
image = image.resize((512, 512))
mask = mask.resize((512,512))
guidance_scale=16
num_samples = 1
prompt = 'a photo of a ' + product_name + ' with ' + target_name + ' product photograpy'
model = replicate.models.get("cjwbw/stable-diffusion-v2-inpainting")
version = model.versions.get("f9bb0632bfdceb83196e85521b9b55895f8ff3d1d3b487fd1973210c0eb30bec")
output = version.predict(prompt=prompt, image=open("bg_removed.png", "rb"), mask=open("blured_mask.png", "rb"))
response = requests.get(output[0])
return Image.open(BytesIO(response.content))
with gr.Blocks() as demo:
gr.Markdown("# Advertise better with AI")
# with gr.Tab("Prompt Paint - Basic"):
with gr.Row():
with gr.Column():
input_image = gr.Image(label = "Upload your product's photo", type = 'pil')
product_name = gr.Textbox(label="Describe your product")
target_name = gr.Textbox(label="Where do you want to put your product?")
# result_prompt = product_name + ' in ' + target_name + 'product photograpy ultrarealist'
image_button = gr.Button("Generate")
with gr.Column():
image_output = gr.Image()
image_button.click(generate_image, inputs=[input_image, product_name, target_name ], outputs=image_output, api_name='test')
demo.launch() |